追踪美国的COVID-19抗议活动。共享任务2:事件数据库复制,CASE 2022

Vanni Zavarella, Hristo Tanev, Ali Hürriyetoǧlu, Peratham Wiriyathammabhum, Bertrand De Longueville
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引用次数: 6

摘要

共享任务2的目标是通过将检测到的事件的时空分布与现有事件数据库进行比较,来评估最先进的事件检测系统。该任务主要关注现实世界场景中事件检测系统的一些可用性需求。也就是说,它旨在衡量这样一个系统的能力:(i)检测新闻和社交媒体中提到的社会政治事件,(ii)适当地找到它们的地理位置,(iii)从多个来源提取的涉及同一实际事件的重复报道。建立一个带注释的语料库来训练和评估这些子任务是非常耗时的。在没有带注释的语料库可用的情况下,间接评估系统输出的一种可能方法是测量其与人工策划的事件数据集的相关性。在过去三年中,COVID-19大流行成为世界范围内限制和抗流行病措施的动力。这在许多国家引发了一波反应和公民行动。共同任务2要求参与者从主流媒体和社交媒体的大型非结构化数据源中识别与COVID-19相关的抗议行动。我们对每个系统在时间和空间上模拟抗议事件演变的能力进行了评估,方法是使用一系列与covid - 19相关的综合且经过验证的抗议事件数据集相关的相关指标(Raleigh et al., 2010)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracking COVID-19 protest events in the United States. Shared Task 2: Event Database Replication, CASE 2022
The goal of Shared Task 2 is evaluating state-of-the-art event detection systems by comparing the spatio-temporal distribution of the events they detect with existing event databases.The task focuses on some usability requirements of event detection systems in real worldscenarios. Namely, it aims to measure the ability of such a system to: (i) detect socio-political event mentions in news and social media, (ii) properly find their geographical locations, (iii) de-duplicate reports extracted from multiple sources referring to the same actual event. Building an annotated corpus for training and evaluating jointly these sub-tasks is highly time consuming. One possible way to indirectly evaluate a system’s output without an annotated corpus available is to measure its correlation with human-curated event data sets.In the last three years, the COVID-19 pandemic became motivation for restrictions and anti-pandemic measures on a world scale. This has triggered a wave of reactions and citizen actions in many countries. Shared Task 2 challenges participants to identify COVID-19 related protest actions from large unstructureddata sources both from mainstream and social media. We assess each system’s ability to model the evolution of protest events both temporally and spatially by using a number of correlation metrics with respect to a comprehensive and validated data set of COVID-related protest events (Raleigh et al., 2010).
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